Data Science and Engineering Manager

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

TRIBE - Teamwork, Respect, Integrity, Bold & Engage are our cultural cornerstone. We respect all our people and aspire to supply inclusive working experiences and an environment that reflects the audience we serve.

We are diverse, we come from different backgrounds and different countries. We are software engineers, designers, researchers, marketers, accountants, customer service, production operatives, technologists and more. We believe that a diverse organisation makes us stronger.

Our Purpose

We at Research Data believe that delivering life-changing Data Services through self-service tools related to Data, Master Data and Analytics, will empower our Customers as well as Music Tribe.?

?We further believe that relentlessly and consistently measuring and improving our services, will lead to appreciation and Customer Advocacy.?

Roles and Responsibilities

Strategy?

• Responsible of world class and passionate data scientists and engineers to develop innovative science products and reusable science modules. You’ll translate complex requirements into detailed project plans and schedules?
• Responsible for designing, implementing, and maintaining systems used to collect and analyse business intelligence data. ?
• Responsible for developing and executing a data integration & automation roadmap as well as delivering an architecture for collecting and storing data. Also, maintaining & testing of data pipeline during development proves (Reliability / Performance)?
• Owning a roadmap to develop innovative science products and reusable science modules, ensuring they are productionised and available to the business. ?
• Develop processes and tools to monitor and analyse model performance and data accuracy?
• Creating and using advanced machine learning algorithms and statistics?
• Using statistical computer languages to manipulate data and draw insights from large data sets?
• Develop custom data models and algorithms to apply to data sets??

Operations?

• Design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services to satisfy business needs?
• Ensuring non-functional system characteristics such as such as security, maintainability, quality, performance, and reliability are captured, prioritized, and incorporated into products?
• Developing reusable software components and systems based on requirements, architecture and design specifications and organizational policies and standards.?
• Collaborating with other data engineers, scientists, software architects, software engineers, quality engineers, and other team members to design and build solutions that best fit the requirements?
• Leverage Agile, CI / CD and DevOps methodologies to deliver high quality product on-time?
• Expose data to end users using Power BI which can be done by BI team, Azure API Apps or other modern visualization platforms like Rstudio, Panda?
• Maintain data so that it remains available and usable e.g. SQL (Basic, Advanced modelling, Efficient, Big Data and Programmatic)?
• Data munging such as reshaping, aggregating, joining disparate sources, etc...small-scale ETL, API interaction, and automation e.g. Python?
• Data normalisation and modelling as part of the transform step of ETL/ELT pipelines?
• Create interfaces and mechanisms for the data flow and access of information?
• Clean the data, removing duplicates, and conforming data to specific data model. Store the normalised data in the data warehouse or in a relational database.??

Qualifications, Minimum

• Bachelor’s degree or equivalent in an engineering/numerate subject e.g. Engineering, Stats, Maths, Computer Sciences?
• Experience in hiring, mentoring and retaining a strong team of motivated data science engineers?
• Experience in full-stack development and applying it to build science products. E.g. could include some or all of C/C++, Python/R, Linux scripting, SQL, Docker coupled with front ends such as HTML, Javascript, web front ends?
• Experience with and passion for developing full-stack solutions using modern technologies, understanding and implementing science and machine learning algorithms such as regularised regression or tree-based ensembles, and ability to implement through libraries?
• Strong understanding of Data Architecture & Modelling, and appreciation of impact on solution design decisions.?
• Strong understanding of ETL/ELT concepts and execution?
• Demonstrative ability to develop complex SQL queries and Stored Procedures?

?Qualifications, Preferred

• Ability to define and drive adoption of best practice in agile software development, quality assurance and testing, including automation?
• Good understanding of machine learning techniques, with applications to classification, regression, and clustering.?
• Demonstrated experience building data lakes and data warehouses in Azure?
• Foundational knowledge of securing data access in a relational database?
• Hands-on experience using Synapse or related tool with cloud-based resources (e.g. Stored Procedures, ADF, NoSQL Databases, JSON/XML data formats)?
• Hands-on experience with Azure Functions, Azure Data Factory data integration techniques?
• Data Modelling concepts, monitoring, designs and techniques?
• Knowledge of Data Warehouse project lifecycle, tools, technologies, best practices?
• Hands on with DWH, APIs (RESTful), Spark APIs, FTP protocols, SSL, SFTP, PKI (public Key Infrastructure) and Integration testing??

Tools

• Full stack of Azure: Azure Datalake Gen-2, Azure Synapse, Azure Data Factory, Power BI, Power Automate, Azure Storage Explorer, Azure Cosmos DB, Azure Databricks, Azure Data Studio), Python/R, MDM Profisee (a plus), Azure D365, Apache Spark??

Metrics

• Data Enrichment: Each modeler should ask themselves about “is that all the relevant data? Are there, maybe outside of the organization, additional sources that could generate impactful features?”?
• Support & Coverage: Support is defined as the percentage of keys found in a secondary dataset, out of all unique keys in the main dataset. Coverage is defined as the percentage of rows with a matching key in the main dataset, out of all rows in the main dataset.?
• Data Quality: can be measured by number of incidents triggered by data issues. Includes: Internal alerts, failed tests, issues raised by BI consumers. Will need to include scoring weighting for severity of incident and resolution time = additional complexity?
• Data On-time delivery (Data Uptime): Expected frequency (Daily, Hourly, RT) or SLA requirements (Frequency clause stipulated by an SLA agreement between a data producer and consumer for when data must by updated by e.g. every day at 7am or every 20 min.?
• Development Velocity: Number of user story points completed per iteration; Data research & exploration can be expressed in small, testable user stories and epics = efficiency of the team?
• Data Science: Data model’s freshness / Data pipeline’s speed / Machine learning model’s accuracy

Why work for us?

• Annual leave provision, plus public holidays
• Pension / retirement fund contributions
• Health Care
• Hybrid & remote working options in some locations
• We measure our People Engagement
• We run quarterly team building events
• We are invested in learning & development
• We reward daily through digital recognition systems